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An Approach to Building High-Quality Tag Hierarchies from Crowdsourced Taxonomic Tag Pairs

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Social Informatics (SocInfo 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8238))

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Abstract

Building taxonomies for web content is costly. An alternative is to allow users to create folksonomies, collective social classifications. However, folksonomies lack structure and their use for searching and browsing is limited. Current approaches for acquiring latent hierarchical structures from folksonomies have had limited success. We explore whether asking users for tag pairs, rather than individual tags, can increase the quality of derived tag hierarchies. We measure the usability cost, and in particular cognitive effort required to create tag pairs rather than individual tags. Our results show that when applied to tag pairs a hierarchy creation algorithm (Heymann-Benz) has superior performance than when applied to individual tags, and with little impact on usability. However, the resulting hierarchies lack richness, and could be seen as less expressive than those derived from individual tags. This indicates that expressivity, not usability, is the limiting factor for collective tagging approaches aimed at crowdsourcing taxonomies.

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References

  1. O’Reilly, T.: What is web 2.0: design patterns and business models for the next generation of software (2005), http://oreilly.com/web2/archive/what-is-web-20.html (June 20, 2013)

  2. Vander Wal, T.: Folksonomy Coinage and Definition (2007), http://vanderwal.net/folksonomy.html

  3. Strohmaier, M., Helic, D., Benz, D., Körner, C., Kern, R.: Evaluation of Folksonomy Induction Algorithms. ACM Transactions on Intelligent Systems and Technology 3(4), Article 74 (2012)

    Google Scholar 

  4. Golder, S., Huberman, B.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)

    Article  Google Scholar 

  5. Guy, M., Tonkin, E.: Tidying up tags. D-Lib Magazine 12(1) (January 2006) ISSN 1082-9873

    Google Scholar 

  6. Heymann, P., Garcia-Molinay, H.: Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems. InfoLab Technical Report, Stanford (2006)

    Google Scholar 

  7. Plangprasopchok, A., Lerman, K., Getoor, L.: From saplings to a tree: Integrating structured metadata via relational affinity propagation. In: Proceedings of the AAAI Workshop on Statistical Relational AI, Menlo Park, CA, USA (2010)

    Google Scholar 

  8. Benz, D., Hotho, A., Stutzer, S.: Semantics made by you and me: Self-emerging ontologies cancapture the diversity of shared knowledge. In: 2nd Web Science Conference (WebSci 2010), Raleigh, NC, USA (2010)

    Google Scholar 

  9. Laniado, D., Eynard, D., Colombetti, M.: Using WordNet to turn a folksonomy into a hierarchy of concepts. In: 4th Italian Semantic Web Workshop: Semantic Web Application and Perspectives, Bari, Italy, pp. 192–201 (2007)

    Google Scholar 

  10. Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1), 5–15 (2007)

    Article  MathSciNet  Google Scholar 

  11. Zheng, H., Wu, X., Yu, Y.: Enriching WordNet with Folksonomies. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 1075–1080. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Lin, H., Davis, J.: Computational and crowdsourcing methods for extracting ontological structure from folksonomy. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 472–477. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Solskinnsbakk, G., Gulla, J.: Mining tag similarity in folksonomies. In: 3rd International Workshop on Search and Mining User-generated Contents (SMUC 2011), Glasgow, Scotland, pp. 53–60 (2011)

    Google Scholar 

  14. Schmitz, C., Hotho, A., Jäschke, R., Stumme, G.: Mining association rules in folksonomies. In : 10th IFCS Conference: Studies in Classification, Data Analysis and Knowledge Organization, Ljubljana, Slovenia, pp.261-270 (2006)

    Google Scholar 

  15. Schmitz, P.: Inducing ontology from flickr tags. In: Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland (2006)

    Google Scholar 

  16. Sanderson, M., Croft, B.: Deriving concept hierarchies from text. In: 22nd ACM Conference of the Special Interest Group in Information Retrieval, Berkeley, California, USA, pp. 206–213 (1999)

    Google Scholar 

  17. Frey, B., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Plangprasopchok, A., Lerman, K.: Constructing Folksonomies from User-Specified Relations on Flickr. In: 18th International World Wide Web Conference, Madrid, Spain, pp. 781–790 (2009)

    Google Scholar 

  19. Andrews, P., Pane, J.: Sense induction in folksonomies: a review. Artificial Intelligence Review, 1-28 (2012)

    Google Scholar 

  20. Brooke, J.: SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189–194 (1996)

    Google Scholar 

  21. Tullis, T., Stetson, J.: A comparison of questionnaires for assessing website usability. In: Usability Professional Association Conference, Minneapolis, USA, pp. 1–12 (2004)

    Google Scholar 

  22. Bangor, A., Kortum, P., Miller, J.: An empirical evaluation of the system usability scale. International Journal of Human-Computer Interaction 24(6), 574–594 (2008)

    Article  Google Scholar 

  23. Dellschaft, K., Staab, S.: On how to perform a gold standard based evaluation of ontology learning. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 228–241. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  24. McLellan, S., Muddimer, A., Peres, S.: The Effect of Experience on System Usability Scale Ratings. Journal of Usability Studies 7(2), 56–67 (2012)

    Google Scholar 

  25. Halpin, H., Robu, V., Shepherd, H.: The Complex Dynamics of Collaborative Tagging. In: 6th International Conference on the World Wide Web, Banff, Canada, pp. 211–220 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Electronics and Computer Science, University of Southampton, Southampton, United Kingdom

    Fahad Almoqhim, David E. Millard & Nigel Shadbolt

Authors
  1. Fahad Almoqhim
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  2. David E. Millard
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  3. Nigel Shadbolt
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Editor information

Editors and Affiliations

  1. Department of Social Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, 606-8501, Sakyo-ku, Kyoto, Japan

    Adam Jatowt

  2. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim  & Bing Tian Dai  & 

  3. Department of Information and Library Science, School of Informatics and Computing, Indiana University, 1320 E. 10th St., LI 011, 47405, Bloomington, IN, USA

    Ying Ding

  4. Department of Integrated Psychological Sciences, Kwansei Gakuin University, 1-1-155 Uegahara, 662-8501, Nishinomiya, Hyogo, Japan

    Asako Miura

  5. Faculty of Library, Information and Media Science, University of Tsukuba, 1-2 Kasuga, 305-8550, Tsukuba City, Ibaraki, Japan

    Taro Tezuka

  6. Department of Computer Science, University of Caen Basse-Normandie, Campus Côte de Nacre, F-14032, Caen Cedex, France

    Gaël Dias

  7. Department of Social Informatics, Graduate School of Informatics,, Kyoto University, 606-8501, Kyoto, Japan

    Katsumi Tanaka

  8. Department of Communication, University of California, 93106, Santa Barbara, CA, USA

    Andrew Flanagin

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Almoqhim, F., Millard, D.E., Shadbolt, N. (2013). An Approach to Building High-Quality Tag Hierarchies from Crowdsourced Taxonomic Tag Pairs. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-03260-3_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03259-7

  • Online ISBN: 978-3-319-03260-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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